Autonomous Unmanned Aerial Vehicles in Search and Rescue Missions Using Real-Time Cooperative Model Predictive...

Area of Science:

• Robotics and Automation
• Aerospace Engineering
• Search and Rescue Operations

Background:

• Unmanned Aerial Vehicles (UAVs) offer advantages in cost, deployment, and operator limitations for Search and Rescue (SAR).
• Traditional SAR missions face challenges with human resources and operator perception.
• UAVs present a versatile and efficient alternative for complex SAR scenarios.

Purpose of the Study:

• To propose a real-time path-planning solution for multiple cooperative UAVs in SAR missions.
• To optimize search patterns according to international SAR standards.
• To develop an embedded, on-board navigation system for cooperative UAVs.

Main Methods:

• Utilized Particle Swarm Optimization (PSO) to solve a Model Predictive Control (MPC) problem.
• Incorporated a coordinated turn kinematic model for level flight with wind effects into the MPC.
• Implemented the solution for on-board UAV computers using DUNE navigation software.
• Evaluated performance through Software-In-The-Loop (SITL) simulations with Ardupilot and JSBSim.

Main Results:

• The cooperative multi-UAV system demonstrated effective real-time path planning for SAR.
• Simulations confirmed the system's ability to adhere to SAR directives.
• A group of three UAVs achieved 50% Probability of Success 2.35 times faster than a single UAV.

Conclusions:

• Cooperative multi-UAV systems significantly improve SAR mission efficiency.
• The proposed PSO-MPC approach provides a viable real-time path-planning solution for autonomous SAR.
• Embedded implementation on UAVs with DUNE software is feasible and effective.